Impact of Coronavirus Pandemic on Immunocompromised Patients
Bibliographic record
Abstract
Background: The coronavirus (COVID-19) pandemic has been stressful for everyone across the globe and even more so for the immunocompromised population, bringing with it an enormous emotional impact on their lives. Fear and anxiety regarding this novel disease created a state of panic among the public. The aim was to review published cases of COVID-19 and to discuss pandemic related anxiety and stress in immunocompromised populations and describe their presentations, diagnostic processes, clinical characteristics and outcomes. Methods: Using specific keywords, a review of scientific literature was conducted in databases such as PubMed, Google Scholar including systematic reviews, meta-analysis, case series, and case reports. Of 35 articles, 22 studies were reviewed. Results: Of the 22 studies involved, a few of the studies had significant data. Among 603 ovarian cancer patients, 88.6% were worried, 51.4% anxious, and 26.5% depressed due to COVID-19 related delay in care. Among 167 people living with HIV patients, 25% reported generalized anxiety disorder. In another cross-sectional study with 500 respondents, anxiety (aOR = 1.73; 95% CI, 1.25–2.40, p-value = 0.001), depression and anxiety (aOR = 1.80; 95% CI, 1.28–2.53, p-value <0.001), and mental health deterioration (aOR = 1.94; 95% CI, 1.48–2.55, p-value <0.001) on basis of fear of the COVID pandemic was noted. Conclusion: As a conclusion, these articles demonstrated that patients with immunocompromised states had more symptoms of anxiety and fear as compared to the immunocompetent patients. Most of the patients had concerns of fear about future health implications, concern over social implications, and possible financial difficulties encountered and remained infectious for a longer duration with severe anxiety symptoms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".